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AbstractWe draw upon recent advances that combine causal inferences with machine learning, to show that poverty is the key income distribution measure that matters for development outcomes. In a predictive framework, we first show that LASSO chooses only the headcount measure of poverty from 37 income distribution measures in predicting schooling, institutional quality, and per capita income. Next, causal inferences with post‐LASSO models indicate that poverty matters more strongly for development outcomes than does the Gini coefficient. Finally, instrumental variable estimates in conjunction with post‐LASSO models show that compared to Gini, poverty is more strongly causally associated with schooling and per capita income, but not institutional quality. Our results question the literature's overwhelming focus on the Gini coefficient. At the least, our results imply that the causal link from inequality (as measured by Gini) to development outcomes is tenuous.

Search, broadly defined, is a critical managerial activity. Our contribution is a model of search for multiattribute alternatives, and our focus is on parallel search, where the decision is about the number of alternatives to explore. Most of the search literature considers univariate alternatives, and it can be applied to a multiattribute setting provided that the trade-offs to be used at the final selection stage were known at the search stage. However, uncertainty about trade-offs is likely to occur, especially in settings that involve parallel search (e.g., vendor selection, new product development, innovation tournaments). We show that incorporating uncertainty about trade-offs into a model changes its search strategy recommendations. Failing to account for such uncertainty, which is likely in practice, leads to suboptimal search and potentially large losses. For parallel search and a multivariate elliptical (e.g., normal) distribution of the alternatives, the solution is equivalent to univariate search with appropriately adjusted standard deviation. We prove that, in this setting, the optimal number of alternatives to explore increases if uncertainty about trade-offs increases, and we discuss the value of information about uncertain trade-offs.

Targets are used quite often as a management tool, and it has been argued that thinking in terms of targets may be more natural than thinking in terms of utilities. The standard expected-utility framework with a single attribute (such as money) and nondecreasing, bounded utility is equivalent to a target-oriented setting. A utility function, properly scaled, can be expressed as a cumulative distribution function (cdf) and related to the probability of meeting a target value. We consider whether the equivalence of the two approaches extends to the case of multiattribute utility. Our analysis shows that a multiattribute utility function cannot always be expressed in the form of a cumulative distribution function and, furthermore, cannot always be expressed in the form of a target-oriented utility function. However, in each case equivalence does hold for certain well-known classes of utility functions. In general, our results imply that although interpreting utility as a cdf and thinking about achieving targets works fine in the case of a single attribute, this approach should be used with caution in the multiattribute case, with cdf representations requiring more caution than target-oriented representations.

When combining forecasts, a simple average of the forecasts performs well, often better than more sophisticated methods. In a prescriptive spirit, we consider some other parsimonious, easy-to-use heuristics for combining interval forecasts and compare their performance with the benchmark provided by the simple average, using simulations from a model we develop and data sets with forecasts made by professionals in their domain of expertise. We find that the empirical results closely match the results from our model, thus providing some validation for the theoretical model. The relative performance of the heuristics is influenced by the degree of overconfidence in and dependence among the individual forecasts, and different heuristics come out on top under different circumstances. The results provide some good, easy-to-use alternatives to the simple average with an indication of the conditions under which each might be preferable, enabling us to conclude with some prescriptive advice.